import torch import torch.nn as nn import torch.nn.functional as F import math from linear_attention_transformer import LinearAttentionTransformer def get_torch_trans(heads=8, layers=1, channels=64): encoder_layer = nn.TransformerEncoderLayer( d_model=channels, nhead=heads, dim_feedforward=64, activation="gelu" ) return nn.TransformerEncoder(encoder_layer, num_layers=layers) def get_linear_trans(heads=8,layers=1,channels=64,localheads=0,localwindow=0): return LinearAttentionTransformer( dim = channels, depth = layers, heads = heads, max_seq_len = 256, n_local_attn_heads = 0, local_attn_window_size = 0, ) def Conv1d_with_init(in_channels, out_channels, kernel_size): layer = nn.Conv1d(in_channels, out_channels, kernel_size) nn.init.kaiming_normal_(layer.weight) return layer class DiffusionEmbedding(nn.Module): def __init__(self, num_steps, embedding_dim=128, projection_dim=None): super().__init__() if projection_dim is None: projection_dim = embedding_dim self.register_buffer( "embedding", self._build_embedding(num_steps, embedding_dim / 2), persistent=False, ) self.projection1 = nn.Linear(embedding_dim, projection_dim) self.projection2 = nn.Linear(projection_dim, projection_dim) def forward(self, diffusion_step): x = self.embedding[diffusion_step] x = self.projection1(x) x = F.silu(x) x = self.projection2(x) x = F.silu(x) return x def _build_embedding(self, num_steps, dim=64): steps = torch.arange(num_steps).unsqueeze(1) # (T,1) frequencies = 10.0 ** (torch.arange(dim) / (dim - 1) * 4.0).unsqueeze(0) # (1,dim) table = steps * frequencies # (T,dim) table = torch.cat([torch.sin(table), torch.cos(table)], dim=1) # (T,dim*2) return table class diff_CSDI(nn.Module): def __init__(self, config, inputdim=2): super().__init__() self.channels = config["channels"] self.diffusion_embedding = DiffusionEmbedding( num_steps=config["num_steps"], embedding_dim=config["diffusion_embedding_dim"], ) self.input_projection = Conv1d_with_init(inputdim, self.channels, 1) self.output_projection1 = Conv1d_with_init(self.channels, self.channels, 1) self.output_projection2 = Conv1d_with_init(self.channels, 1, 1) nn.init.zeros_(self.output_projection2.weight) self.residual_layers = nn.ModuleList( [ ResidualBlock( side_dim=config["side_dim"], channels=self.channels, diffusion_embedding_dim=config["diffusion_embedding_dim"], nheads=config["nheads"], is_linear=config["is_linear"], ) for _ in range(config["layers"]) ] ) def forward(self, x, cond_info, diffusion_step): B, inputdim, K, L = x.shape x = x.reshape(B, inputdim, K * L) x = self.input_projection(x) x = F.relu(x) x = x.reshape(B, self.channels, K, L) diffusion_emb = self.diffusion_embedding(diffusion_step) skip = [] for layer in self.residual_layers: x, skip_connection = layer(x, cond_info, diffusion_emb) skip.append(skip_connection) x = torch.sum(torch.stack(skip), dim=0) / math.sqrt(len(self.residual_layers)) x = x.reshape(B, self.channels, K * L) x = self.output_projection1(x) # (B,channel,K*L) x = F.relu(x) x = self.output_projection2(x) # (B,1,K*L) x = x.reshape(B, K, L) return x, 0 class ResidualBlock(nn.Module): def __init__(self, side_dim, channels, diffusion_embedding_dim, nheads, is_linear=False): super().__init__() self.diffusion_projection = nn.Linear(diffusion_embedding_dim, channels) self.cond_projection = Conv1d_with_init(side_dim, 2 * channels, 1) self.mid_projection = Conv1d_with_init(channels, 2 * channels, 1) self.output_projection = Conv1d_with_init(channels, 2 * channels, 1) self.is_linear = is_linear if is_linear: self.time_layer = get_linear_trans(heads=nheads,layers=1,channels=channels) self.feature_layer = get_linear_trans(heads=nheads,layers=1,channels=channels) else: self.time_layer = get_torch_trans(heads=nheads, layers=1, channels=channels) self.feature_layer = get_torch_trans(heads=nheads, layers=1, channels=channels) def forward_time(self, y, base_shape): B, channel, K, L = base_shape if L == 1: return y y = y.reshape(B, channel, K, L).permute(0, 2, 1, 3).reshape(B * K, channel, L) if self.is_linear: y = self.time_layer(y.permute(0, 2, 1)).permute(0, 2, 1) else: y = self.time_layer(y.permute(2, 0, 1)).permute(1, 2, 0) y = y.reshape(B, K, channel, L).permute(0, 2, 1, 3).reshape(B, channel, K * L) return y def forward_feature(self, y, base_shape): B, channel, K, L = base_shape if K == 1: return y y = y.reshape(B, channel, K, L).permute(0, 3, 1, 2).reshape(B * L, channel, K) if self.is_linear: y = self.feature_layer(y.permute(0, 2, 1)).permute(0, 2, 1) else: y = self.feature_layer(y.permute(2, 0, 1)).permute(1, 2, 0) y = y.reshape(B, L, channel, K).permute(0, 2, 3, 1).reshape(B, channel, K * L) return y def forward(self, x, cond_info, diffusion_emb): B, channel, K, L = x.shape base_shape = x.shape x = x.reshape(B, channel, K * L) diffusion_emb = self.diffusion_projection(diffusion_emb).unsqueeze(-1) # (B,channel,1) y = x + diffusion_emb y = self.forward_time(y, base_shape) y = self.forward_feature(y, base_shape) # (B,channel,K*L) y = self.mid_projection(y) # (B,2*channel,K*L) _, cond_dim, _, _ = cond_info.shape cond_info = cond_info.reshape(B, cond_dim, K * L) cond_info = self.cond_projection(cond_info) # (B,2*channel,K*L) # RuntimeError: Given groups=1, weight of size [128, 28, 1], expected input[64, 145, 672] to have 28 channels, but got 145 channels instead y = y + cond_info gate, filter = torch.chunk(y, 2, dim=1) y = torch.sigmoid(gate) * torch.tanh(filter) # (B,channel,K*L) y = self.output_projection(y) residual, skip = torch.chunk(y, 2, dim=1) x = x.reshape(base_shape) residual = residual.reshape(base_shape) skip = skip.reshape(base_shape) return (x + residual) / math.sqrt(2.0), skip